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雷达卡
摘要翻译：
我们最近提出了一种利用通信系统的信道均衡块来感知环境变化的方案。我们称之为通信感应，简称Commense。在取得了一些初步的积极结果后，我们尝试使用我们的全球移动通信系统(GSM)为基础的CommSense系统进行穿墙传感应用。由于系统固有的高度不确定性，我们使用统计机器学习技术来帮助我们在墙后实验中感知环境变化。我们观察到，在每GSM帧577{\\mu}s的有限数据量下，通过墙可以检测到一个人，准确率为77.458%，携带武器的人可以检测到准确率为95.208%。我们给出了实验的细节和我们在这篇文章中获得的令人鼓舞的结果。
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英文标题：
《GSM-CommSense-based through-the-wall sensing》
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作者：
Abhishek Bhatta and Amit Kumar Mishra
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最新提交年份：
2017
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
We have recently proposed a scheme to use the channel equalization blocks of telecommunication systems to sense changes in an environment. We call this communication-sensing, CommSense for short. After some initial positive results we tried to use our global system for mobile communication (GSM) based CommSense system for a through-the-wall sensing application. As the system was inherently highly under-determined we used statistical machine learning techniques to help us sense environmental changes in the behind-the-wall experiments. We observed that with limited amount of data per GSM frame of 577 {\mu}s a person can be detected across a wall to an accuracy of 77.458% and a person carrying a weapon can be detected to an accuracy of 95.208%. We present details of the experiments and the encouraging results that we have obtained in this article.
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PDF链接：
https://arxiv.org/pdf/1712.08574
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